Calculate value of management flexibility

What it's for

Gives boards and investors a defensible answer to 'what is the option value of waiting and adapting?' while visibly separating real flexibility from perfect hindsight.

What you give it

Inputs split into evidence read from your connected systems, calibration your team owns, and numerical controls that affect precision but never the result's meaning.

{
  "maximum_adaptive_liquidity_breach_probability": 0,
  "minimum_liquidity_buffer": 20,
  "policy_integrity_supported": true,
  "scenarios": [
    {
      "adaptive_minimum_liquidity": 20,
      "adaptive_net_value": 55,
      "evidence_verified": true,
      "id": "adverse",
      "perfect_information_net_value": 70,
      "probability": 0.4,
      "static_minimum_liquidity": -10,
      "static_net_value": 30
    },
    {
      "adaptive_minimum_liquidity": 20,
      "adaptive_net_value": 120,
      "evidence_verified": true,
      "id": "favorable",
      "perfect_information_net_value": 130,
      "probability": 0.6,
      "static_minimum_liquidity": 20,
      "static_net_value": 100
    }
  ],
  "tail_probability": 0.4
}

What you get back

This is the actual output of running the example above — computed by the same function the platform calls, not an illustration.

{
  "configuration": {
    "dominance_tolerance": 0,
    "maximum_adaptive_liquidity_breach_probability": 0,
    "maximum_tail_adaptive_regret": 1000000000000000000,
    "maximum_unverified_probability_mass": 0,
    "minimum_expected_flexibility_value": 0,
    "minimum_liquidity_buffer": 20,
    "policy_integrity_supported": true,
    "tail_probability": 0.4
  },
  "decision": "management_flexibility_value_supported",
  "failed_gates": [],
  "guardrails": [
    "Static, adaptive and perfect-information values must be evaluated on the same coherent scenarios, prices, horizon, discounting and full action costs. Independently sorted distributions cannot price flexibility.",
    "The adaptive policy must first pass a nonanticipativity and information-timing audit. Any value obtained from acting on a signal before it is observable is clairvoyance and is excluded from supported management flexibility.",
    "Perfect information is an unattainable upper bound, not a forecast. Scenario value does not prove realized or causal value; validate deployed policies prospectively and reconcile outcomes to finance records."
  ],
  "method": "common_scenario_flexibility_evsi_evpi_and_tail_regret",
  "scenario_diagnostics": [
    {
      "adaptive_liquidity_breach": false,
      "adaptive_net_value": 55,
      "adaptive_regret": 15,
      "adaptive_underperformance": 0,
      "evidence_verified": true,
      "flexibility_value": 25,
      "perfect_information_net_value": 70,
      "probability": 0.4,
      "scenario_id": "adverse",
      "static_liquidity_breach": true,
      "static_net_value": 30
    },
    {
      "adaptive_liquidity_breach": false,
      "adaptive_net_value": 120,
      "adaptive_regret": 10,
      "adaptive_underperformance": 0,
      "evidence_verified": true,
      "flexibility_value": 20,
      "perfect_information_net_value": 130,
      "probability": 0.6,
      "scenario_id": "favorable",
      "static_liquidity_breach": false,

How it works

Constrained optimization — Pick the best feasible option under real limits — budget, headcount, dependencies, capacity — rather than ranking a list and hoping it fits.

1. 1 Freeze scenario probabilities and full-cost static, adaptive and perfect-information net values plus minimum liquidity on the same horizon, currency, price and discount basis.
2. 2 Require an upstream scenario-tree integrity clearance, verify the perfect-information bound scenario by scenario, and compute expected flexibility, EVPI, remaining information value and the fraction of the attainable information advantage captured by executable adaptation.
3. 3 Measure adaptive underperformance probability, tail-CVaR underperformance and regret, static/adaptive liquidity breaches and unverified probability mass; support value only when every governed economic, risk and evidence gate clears.

Before you trust it

Every tool in the catalog ships with the conditions under which its answer is meaningful — and the conditions under which it should abstain instead of guessing.

Calibrating it to your org

Calibration workflow

1. 1 Define the management decision, target outcome, aggregate unit, privacy boundary, cadence, and prediction/intervention horizon for this organization.
2. 2 Build a tenant-scoped historical cohort using only information available before each prediction or decision; preserve zero periods, censoring, assignment probabilities, and unresolved outcomes when the method requires them.
3. 3 Estimate statistical parameters on training history, but obtain costs, utilities, risk tolerance, practical-effect thresholds, capacity, and policy constraints from accountable decision owners.
4. 4 Validate on later time windows or held-out aggregate units at the deployment grain, against a simple baseline and the function-specific validation strategy.
5. 5 Deploy only if the returned decision clears evidence, overlap, calibration, robustness, and guardrail checks; warning, unsupported, schema-gap, and fallback decisions are abstentions.
6. 6 Monitor realized outcomes, data drift, coverage, and decision regret; recalibrate at a governed cadence or after a detected regime/definition change, never merely because a stakeholder dislikes the result.

Call it from your AI

You don't wire up 388 tools in your MCP client. The GitRevio MCP server exposes 18 tools, three of which let an agent search the catalog, read a tool's schema, and run it — so the assistant finds this one on its own.

gitrevio_capabilities_search
  { "q": "price only executable management flexibility on" }
  → finds "calculate_value_of_management_flexibility"

gitrevio_capability_describe
  { "capability_id": "calculate_value_of_management_flexibility" }
  → returns the input schema and agent guidance shown on this page

gitrevio_capability_run
  { "capability_id": "calculate_value_of_management_flexibility", "arguments": { ... } }
  → returns the result shown above

Works in Claude Desktop, Claude Code, Cursor, Cline, Continue.dev, Goose and Aider. See the MCP server.

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